Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.

Spectral reflectance of ODP Leg 178 holes

The routine use of spectrophotometry on the sediment surfaces of archive halves of each section during the onboard sedimentological core description process is a great stride toward development of real-time noninvasive characterization of deep-sea sediments. Spectral reflectance data have been used so far for mineral composition studies as well as for lithostratigraphic correlation between sites (Balsam and Deaton, 1991; Balsam et al., 1997; Mix et al., 1995; Ortiz et al., 1999). Their results demonstrate that spectrophotometry can estimate CaCO3 content by using the 4.65-, 5.25-, and 5.55-µm wavelength spectrums. A detailed overview of various other noninvasive methods is given in Ortiz and Rack (1999). The purpose of this study is to test whether spectrophotometry in the visible band can be used as a tool to gather further information about grain-size variation, sorting, compaction, and porosity, which are directly linked to the sedimentation process. From remote sensing data analyses, it is known that diffuse spectral reflectance data in the visible band in the wavelength window of 7.0-6.5 µm are sensitive to grain-size variations. It appears that a relationship between grain size and signal absorption exists only in this wavelength window. (e.g., Clark, 1999; Gaffey, 1986; Gaffey et al., 1993). Variations in grain size during a sedimentation process are linked to depositional energy, which affects sorting, compaction, and porosity of sediment deposits. As an example, we study here the spectrophotometric data of the sedimentary sequence of Hole 1098C, which was deposited under widely varying environmental conditions. Alternating turbidite and finely laminated sediments were recovered from Hole 1098C. The turbidites are related to a high depositional energy environment; the finely laminated sediments are related to a low depositional energy environment. Data from Hole 1098C were therefore used to test whether the spectral reflectance data can provide a proxy for these different depositional environments.

Figures and related data of a review of the "Global and Russian energy outlook up to 2040"

The Global and Russian Energy Outlook up to 2040, prepared by the Energy Research Institute of the Russian Academy of Sciences and the Analytical Center for the Government of the Russian Federation, analyses the long-term changes in the main energy markets and thereby identifies the threats to the Russian economy and energy sector. Research has shown that shifts in the global energy sector, especially in hydrocarbon markets (primarily the development of technologies for shale oil and gas extraction), will result in a slowdown of Russia's economy by one percentage point each year on average due to a decrease in energy exports comparison with the official projections. Owing to the lack of development of an institutional framework, an outdated tax system, low competition and low investment efficiency, Russia will be the most sensitive to fluctuations in global hydrocarbon markets among all major energy market players within the forecast period.